As businesses move to data science to exploit the potential of their data, from creating business intelligence analytics to building artificial intelligence (AI)/machine learning (ML) applications, they are beginning to experience the complexities of bringing such complicated systems to production.
- DataKitchen brings lifecycle discipline to data science operations, helping deliver business insights by enabling the development and deployment of innovative and iterative data analytic pipelines.
Features and Benefits
- Learn what DataOps means, how it is related to Agile, DevOps, and data science.
- Assess DataKitchen's end-to-end DataOps solution. It requires minimal programming and integrates with core data engineering, science, governance and visualization tools currently used by the business.
Key questions answered
- What is DataOps and how does it relate to current data science practices?
- How does DataKitchen tackle the problem of complexity in managing multiple machine learning application lifecycles?
Table of contents
Recommendations for enterprises
Why put DataKitchen on your radar?
The DataKitchen platform
On the roadmap